Job Description

Collaborate with fellow data scientists to implement and productionize machine learning models Write clean, scalable code to integrate models into our tech stack Perform model evaluations and testing to ensure robust, accurate performance Monitor models post-deployment and tweak as needed to improve metrics Troubleshoot issues with model pipelines and infrastructure Contribute to the improvement of our ML architecture and frameworks Prototype and fine-tune LLMs Requirements: 6+ months of industry experience in a ML engineering role Proficiency in Python; experience with frameworks like PyTorch Understanding of ML fundamentals Ability to debug and optimize model performance Strong communication skills and ability to work cross-functionally Attention to detail with a passion for metrics and measurement Experience deploying models on AWS or other cloud platforms (preferred) BS/MS degree in Computer Science, Statistics, Math or related field What will you do: 30 Days Onboard and get familiar with our company systems and tech stack Review existing ML pipelines and models in production Setup development environment on AWS and reproduce/re-train existing models Create your first PR 60 Days Take ownership of maintaining and improving 1-2 existing models Take part in on-call rotations and troubleshoot issues in a timely manner Contribute to ML design discussions on new projects Prototype and fine-tune ML models on AWS 90 Days Demonstrate impact by shipping high-quality projects Deploy an ML model end-to-end Fine-tune LLMs on AWS using compute efficient techniques Identify areas for improvement and growth About you: Youre the type of engineer who digs into problems hands-on and fixes things You can focus on the small details but also see the full picture You get excited when you figure out hard ML challenges You can geek out with fellow data scientists but also explain things simply to other teams Youre always learning - whether its the latest research or new engineering skills

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